{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Table of Contents](./table_of_contents.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble Kalman Filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division, print_function\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <style>\n",
       "        .output_wrapper, .output {\n",
       "            height:auto !important;\n",
       "            max-height:100000px; \n",
       "        }\n",
       "        .output_scroll {\n",
       "            box-shadow:none !important;\n",
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#format the book\n",
    "import book_format\n",
    "book_format.set_style()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> I am not well versed with Ensemble filters. I have implemented one for this book, and made it work, but I have not used one in real life. Different sources use slightly different forms of these equations. If I implement the equations given in the sources the filter does not work. It is possible that I am doing something wrong. However, in various places on the web I have seen comments by people stating that they do the kinds of things I have done in my filter to make it work. In short, I do not understand this topic well, but choose to present my lack of knowledge rather than to gloss over the subject. I hope to master this topic in the future and to author a more definitive chapter. At the end of the chapter I document my current confusion and questions. In any case if I got confused by the sources perhaps you also will, so documenting my confusion can help you avoid the same.\n",
    "\n",
    "\n",
    "The ensemble Kalman filter (EnKF) is very similar to the unscented Kalman filter (UKF) of the last chapter. If you recall, the UKF uses a set of deterministically chosen weighted sigma points passed through nonlinear state and measurement functions. After the sigma points are passed through the function, we find the mean and covariance of the points and use this as the filter's new mean and covariance. It is only an approximation of the true value, and thus suboptimal, but in practice the filter is highly accurate. It has the advantage of often producing more accurate estimates than the EKF does, and also does not require you to analytically derive the linearization of the state and measurement equations. \n",
    "\n",
    "The ensemble Kalman filter works in a similar way, except it uses a *Monte Carlo* method to choose a large numbers of sigma points. It came about from the geophysical sciences as an answer for the very large states and systems needed to model things such as the ocean and atmosphere. There is an interesting article on it's development in weather modeling in *SIAM News* [1]. The filter starts by randomly generating a large number of points distributed about the filter's initial state. This distribution is proportional to the filter's covariance $\\mathbf{P}$. In other words 68% of the points will be within one standard deviation of the mean, 95% percent within two standard deviations, and so on. Let's look at this in two dimensions. We will use `numpy.random.multivariate_normal()` function to randomly create points from a multivariate normal distribution drawn from the mean (5, 3) with the covariance\n",
    "\n",
    "$$\\begin{bmatrix}\n",
    "32 & 15 \\\\ 15 & 40\n",
    "\\end{bmatrix}$$\n",
    "\n",
    "I've drawn the covariance ellipse representing two standard deviations to illustrate how the points are distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy.random import multivariate_normal\n",
    "from filterpy.stats import (covariance_ellipse, \n",
    "                            plot_covariance_ellipse)\n",
    "\n",
    "mean = (5, 3)\n",
    "P = np.array([[32, 15],\n",
    "              [15., 40.]])\n",
    "\n",
    "x,y = multivariate_normal(mean=mean, cov=P, size=2500).T\n",
    "plt.scatter(x, y, alpha=0.3, marker='.')\n",
    "plt.axis('equal')\n",
    "\n",
    "plot_covariance_ellipse(mean=mean, cov=P,\n",
    "                        variance=2.**2,\n",
    "                        facecolor='none')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As I already stated, when the filter is initialized a large number of sigma points are drawn from the initial state ($\\mathbf{x}$) and covariance ($\\mathbf{P}$). From there the algorithm proceeds very similarly to the UKF. During the prediction step the sigma points are passed through the state transition function, and then perturbed by adding a bit of noise to account for the process noise. During the update step the sigma points are translated into measurement space by passing them through the measurement function, they are perturbed by a small amount to account for the measurement noise. The Kalman gain is computed from the \n",
    "\n",
    "We already mentioned the main difference between the UKF and EnKF - the UKF choses the sigma points deterministically. There is another difference, implied by the algorithm above. With the UKF we generate new sigma points during each predict step, and after passing the points through the nonlinear function we reconstitute them into a mean and covariance by using the *unscented transform*. The EnKF keeps propagating the originally created sigma points; we only need to compute a mean and covariance as outputs for the filter! \n",
    "\n",
    "Let's look at the equations for the filter. As usual, I will leave out the typical subscripts and superscripts; I am expressing an algorithm, not mathematical functions. Here $N$ is the number of sigma points, $\\chi$ is the set of sigma points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize Step"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\boldsymbol\\chi \\sim \\mathcal{N}(\\mathbf{x}_0, \\mathbf{P}_0)$$\n",
    "\n",
    "This says to select the sigma points from the filter's initial mean and covariance. In code this might look like\n",
    "\n",
    "```python\n",
    "N = 1000\n",
    "sigmas = multivariate_normal(mean=x, cov=P, size=N)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict Step"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\begin{aligned}\n",
    "\\boldsymbol\\chi &= f(\\boldsymbol\\chi, \\mathbf{u}) + v_Q \\\\\n",
    "\\mathbf{x} &= \\frac{1}{N} \\sum_1^N \\boldsymbol\\chi\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "That is short and sweet, but perhaps not entirely clear. The first line passes all of the sigma points through a use supplied state transition function and then adds some noise distributed according to the $\\mathbf{Q}$ matrix. In Python we might write\n",
    "\n",
    "```python\n",
    "for i, s in enumerate(sigmas):\n",
    "    sigmas[i] = fx(x=s, dt=0.1, u=0.)\n",
    "\n",
    "sigmas += multivariate_normal(x, Q, N)\n",
    "```\n",
    "\n",
    "The second line computes the mean from the sigmas. In Python we will take advantage of `numpy.mean` to do this very concisely and quickly.\n",
    "\n",
    "```python\n",
    "x = np.mean(sigmas, axis=0)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now optionally compute the covariance of the mean. The algorithm does not need to compute this value, but it is often useful for analysis. The equation is\n",
    "\n",
    "$$\\mathbf{P} = \\frac{1}{N-1}\\sum_1^N[\\boldsymbol\\chi-\\mathbf{x}^-][\\boldsymbol\\chi-\\mathbf{x}^-]^\\mathsf{T}$$\n",
    "\n",
    "$\\boldsymbol\\chi-\\mathbf{x}^-$ is a one dimensional vector, so we will use `numpy.outer` to compute the $[\\boldsymbol\\chi-\\mathbf{x}^-][\\boldsymbol\\chi-\\mathbf{x}^-]^\\mathsf{T}$ term. In Python we might write\n",
    "\n",
    "```python\n",
    "    P = 0\n",
    "    for s in sigmas:\n",
    "        P += outer(s-x, s-x)\n",
    "    P = P / (N-1)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Update Step"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the update step we pass the sigma points through the measurement function, compute the mean and covariance of the sigma points, compute the Kalman gain from the covariance, and then update the Kalman state by scaling the residual by the Kalman gain. The equations are\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\boldsymbol\\chi_h &= h(\\boldsymbol\\chi, u)\\\\\n",
    "\\mathbf{z}_{mean} &= \\frac{1}{N}\\sum_1^N \\boldsymbol\\chi_h \\\\ \\\\\n",
    "\\mathbf{P}_{zz} &= \\frac{1}{N-1}\\sum_1^N [\\boldsymbol\\chi_h - \\mathbf{z}_{mean}][\\boldsymbol\\chi_h - \\mathbf{z}_{mean}]^\\mathsf{T} + \\mathbf{R} \\\\\n",
    "\\mathbf{P}_{xz} &= \\frac{1}{N-1}\\sum_1^N [\\boldsymbol\\chi - \\mathbf{x}^-][\\boldsymbol\\chi_h - \\mathbf{z}_{mean}]^\\mathsf{T} \\\\\n",
    "\\\\\n",
    "\\mathbf{K} &= \\mathbf{P}_{xz} \\mathbf{P}_{zz}^{-1}\\\\ \n",
    "\\boldsymbol\\chi & = \\boldsymbol\\chi + \\mathbf{K}[\\mathbf{z} -\\boldsymbol\\chi_h + \\mathbf{v}_R] \\\\ \\\\\n",
    "\\mathbf{x} &= \\frac{1}{N} \\sum_1^N \\boldsymbol\\chi \\\\\n",
    "\\mathbf{P} &= \\mathbf{P} - \\mathbf{KP}_{zz}\\mathbf{K}^\\mathsf{T}\n",
    "\\end{aligned}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is very similar to the linear KF and the UKF. Let's just go line by line.\n",
    "\n",
    "The first line,\n",
    "\n",
    "$$\\boldsymbol\\chi_h = h(\\boldsymbol\\chi, u),$$\n",
    "\n",
    "just passes the sigma points through the measurement function $h$. We name the resulting points $\\chi_h$ to distinguish them from the sigma points. In Python we could write this as\n",
    "\n",
    "```python\n",
    "sigmas_h = h(sigmas, u)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next line computes the mean of the measurement sigmas.\n",
    "\n",
    "$$\\mathbf{z}_{mean} = \\frac{1}{N}\\sum_1^N \\boldsymbol\\chi_h$$\n",
    "\n",
    "In Python we write\n",
    "\n",
    "```python\n",
    "z_mean = np.mean(sigmas_h, axis=0)\n",
    "```\n",
    "    \n",
    "Now that we have the mean of the measurement sigmas we can compute the covariance for every measurement sigma point, and the *cross variance* for the measurement sigma points vs the sigma points. That is expressed by these two equations\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\mathbf{P}_{zz} &= \\frac{1}{N-1}\\sum_1^N [\\boldsymbol\\chi_h - \\mathbf{z}_{mean}][\\boldsymbol\\chi_h - \\mathbf{z}_{mean}]^\\mathsf{T} + \\mathbf{R} \\\\\n",
    "\\mathbf{P}_{xz} &= \\frac{1}{N-1}\\sum_1^N [\\boldsymbol\\chi - \\mathbf{x}^-][\\boldsymbol\\chi_h - \\mathbf{z}_{mean}]^\\mathsf{T}\n",
    "\\end{aligned}$$\n",
    "\n",
    "We can express this in Python with\n",
    "\n",
    "```python\n",
    "P_zz = 0\n",
    "for sigma in sigmas_h:\n",
    "    s = sigma - z_mean\n",
    "    P_zz += outer(s, s)\n",
    "P_zz = P_zz / (N-1) + R\n",
    "\n",
    "P_xz = 0\n",
    "for i in range(N):\n",
    "    P_xz += outer(self.sigmas[i] - self.x, sigmas_h[i] - z_mean)\n",
    "P_xz /= N-1\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computation of the Kalman gain is straightforward $\\mathbf{K} = \\mathbf{P}_{xz} \\mathbf{P}_{zz}^{-1}$.\n",
    "\n",
    "In Python this is\n",
    "\n",
    "```python\n",
    "K = np.dot(P_xz, inv(P_zz))```\n",
    "\n",
    "Next, we update the sigma points with\n",
    "\n",
    "$$\\boldsymbol\\chi  = \\boldsymbol\\chi + \\mathbf{K}[\\mathbf{z} -\\boldsymbol\\chi_h + \\mathbf{v}_R]$$ \n",
    "\n",
    "Here $\\mathbf{v}_R$ is the perturbation that we add to the sigmas. In Python we can implement this with\n",
    "\n",
    "```python\n",
    "v_r = multivariate_normal([0]*dim_z, R, N)\n",
    "for i in range(N):\n",
    "    sigmas[i] += dot(K, z + v_r[i] - sigmas_h[i])\n",
    "```\n",
    "\n",
    "\n",
    "Our final step is recompute the filter's mean and covariance.\n",
    "\n",
    "```python\n",
    "    x = np.mean(sigmas, axis=0)\n",
    "    P = self.P - dot(K, P_zz).dot(K.T)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation and Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have implemented an EnKF in the `FilterPy` library. It is in many ways a toy. Filtering with a large number of sigma points gives us very slow performance. Furthermore, there are many minor variations on the algorithm in the literature. I wrote this mostly because I was interested in learning a bit about the filter. I have not used it for a real world problem, and I can give no advice on using the filter for the large problems for which it is suited. Therefore I will refine my comments to implementing a very simple filter. I will use it to track an object in one dimension, and compare the output to a linear Kalman filter. This is a filter we have designed many times already in this book, so I will design it with little comment. Our state vector will be\n",
    "\n",
    "$$\\mathbf{x} = \\begin{bmatrix}x\\\\ \\dot{x}\\end{bmatrix}$$\n",
    "\n",
    "The state transition function is\n",
    "\n",
    "$$\\mathbf{F} = \\begin{bmatrix}1&1\\\\0&1\\end{bmatrix}$$\n",
    "\n",
    "and the measurement function is\n",
    "\n",
    "$$\\mathbf{H} = \\begin{bmatrix}1&0\\end{bmatrix}$$\n",
    "\n",
    "The EnKF is designed for nonlinear problems, so instead of using matrices to implement the state transition and measurement functions you will need to supply Python functions. For this problem they can be written as:\n",
    "\n",
    "```python\n",
    "def hx(x):\n",
    "    return np.array([x[0]])\n",
    "\n",
    "def fx(x, dt):\n",
    "    return np.dot(F, x)\n",
    "```\n",
    "\n",
    "One final thing: the EnKF code, like the UKF code, uses a single dimension for $\\mathbf{x}$, not a two dimensional column matrix as used by the linear kalman filter code.\n",
    "\n",
    "Without further ado, here is the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import kf_book.book_plots as bp\n",
    "from numpy.random import randn\n",
    "from filterpy.kalman import EnsembleKalmanFilter as EnKF\n",
    "from filterpy.kalman import KalmanFilter\n",
    "from filterpy.common import Q_discrete_white_noise\n",
    "\n",
    "np.random.seed(1234)\n",
    "\n",
    "def hx(x):\n",
    "    return np.array([x[0]])\n",
    "\n",
    "def fx(x, dt):\n",
    "    return np.dot(F, x)\n",
    "    \n",
    "F = np.array([[1., 1.],[0., 1.]])\n",
    "\n",
    "x = np.array([0., 1.])\n",
    "P = np.eye(2) * 100.\n",
    "enf = EnKF(x=x, P=P, dim_z=1, dt=1., N=20, hx=hx, fx=fx)\n",
    "\n",
    "std_noise = 10.\n",
    "enf.R *= std_noise**2\n",
    "enf.Q = Q_discrete_white_noise(2, 1., .001)\n",
    "\n",
    "kf = KalmanFilter(dim_x=2, dim_z=1)\n",
    "kf.x = np.array([x]).T\n",
    "kf.F = F.copy()\n",
    "kf.P = P.copy()\n",
    "kf.R = enf.R.copy()\n",
    "kf.Q = enf.Q.copy()\n",
    "kf.H = np.array([[1., 0.]])\n",
    "\n",
    "measurements = []\n",
    "results = []\n",
    "ps = []\n",
    "kf_results = []\n",
    "\n",
    "zs = []\n",
    "for t in range (0,100):\n",
    "    # create measurement = t plus white noise\n",
    "    z = t + randn()*std_noise\n",
    "    zs.append(z)\n",
    "\n",
    "    enf.predict()\n",
    "    enf.update(np.asarray([z]))\n",
    "    \n",
    "    kf.predict()\n",
    "    kf.update(np.asarray([[z]]))\n",
    "\n",
    "    # save data\n",
    "    results.append (enf.x[0])\n",
    "    kf_results.append (kf.x[0,0])\n",
    "    measurements.append(z)\n",
    "    ps.append(3*(enf.P[0,0]**.5))\n",
    "\n",
    "results = np.asarray(results)\n",
    "ps = np.asarray(ps)\n",
    "\n",
    "plt.plot(results, label='EnKF')\n",
    "plt.plot(kf_results, label='KF', c='b', lw=2)\n",
    "bp.plot_measurements(measurements)\n",
    "plt.plot (results - ps, c='k',linestyle=':', lw=1, label='1$\\sigma$')\n",
    "plt.plot(results + ps, c='k', linestyle=':', lw=1)\n",
    "plt.fill_between(range(100), results - ps, results + ps, facecolor='y', alpha=.3)\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be a bit difficult to see, but the KF and EnKF start off slightly different, but soon converge to producing nearly the same values. The EnKF is a suboptimal filter, so it will not produce the optimal solution that the KF produces. However, I deliberately chose $N$ to be quite small (20) to guarantee that the EnKF output is quite suboptimal. If I chose a more reasonable number such as 2000 you would be unable to see the difference between the two filter outputs on this graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outstanding Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of this should be considered as *my* questions, not lingering questions in the literature. However, I am copying equations directly from well known sources in the field, and they do not address the discrepancies.\n",
    "\n",
    "First, in Brown [2] we have all sums multiplied by $\\frac{1}{N}$, as in \n",
    "\n",
    "$$ \\hat{x} = \\frac{1}{N}\\sum_{i=1}^N\\chi_k^{(i)}$$\n",
    "\n",
    "The same equation in Crassidis [3] reads (I'll use the same notation as in Brown, although Crassidis' is different)\n",
    "\n",
    "$$ \\hat{x} = \\frac{1}{N-1}\\sum_{i=1}^N\\chi_k^{(i)}$$\n",
    "\n",
    "The same is true in both sources for the sums in the computation for the covariances. Crassidis, in the context of talking about the filter's covariance, states that $N-1$ is used to ensure an unbiased estimate. Given the following standard equations for the mean and standard deviation (p.2 of Crassidis), this makes sense for the covariance.\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\mu &= \\frac{1}{N}\\sum_{i=1}^N[\\tilde{z}(t_i) - \\hat{z}(t_i)] \\\\\n",
    " \\sigma^2 &= \\frac{1}{N-1}\\sum_{i=1}^N\\{[\\tilde{z}(t_i) - \\hat{z}(t_i)] - \\mu\\}^2\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "However, I see no justification or reason to use $N-1$ to compute the mean. If I use $N-1$ in the filter for the mean the filter does not converge and the state essentially follows the measurements without any filtering. However, I do see a reason to use it for the covariance as in Crassidis, in contrast to Brown. Again, I support my decision empirically - $N-1$ works in the implementation of the filter, $N$ does not.\n",
    "\n",
    "My second question relates to the use of the $\\mathbf{R}$ matrix. In Brown $\\mathbf{R}$ is added to $\\mathbf{P}_{zz}$ whereas it isn't in Crassidis and other sources. I have read on the web notes by other implementers that adding R helps the filter, and it certainly seems reasonable and necessary to me, so this is what I do. \n",
    "\n",
    "My third question relates to the computation of the covariance $\\mathbf{P}$. Again, we have different equations in Crassidis and Brown. I have chosen the implementation given in Brown as it seems to give me the  behavior that I expect (convergence of $\\mathbf{P}$ over time) and it closely compares to the form in the linear KF. In contrast I find the equations in Crassidis do not seem to converge much.\n",
    "\n",
    "My fourth question relates to the state estimate update. In Brown we have \n",
    "\n",
    "$$\\boldsymbol\\chi  = \\boldsymbol\\chi + \\mathbf{K}[\\mathbf{z} -\\mathbf{z}_{mean} + \\mathbf{v}_R]$$ \n",
    "\n",
    "whereas in Crassidis we have\n",
    "\n",
    "$$\\boldsymbol\\chi  = \\boldsymbol\\chi + \\mathbf{K}[\\mathbf{z} -\\boldsymbol\\chi_h + \\mathbf{v}_R]$$ \n",
    "\n",
    "To me the Crassidis equation seems logical, and it produces a filter that performs like the linear KF for linear problems, so that is the formulation that I have chosen. \n",
    "\n",
    "I am not comfortable saying either book is wrong; it is quite possible that I missed some point that makes each set of equations work. I can say that when I implemented them as written I did not get a filter that worked. I define \"work\" as performs essentially the same as the linear KF for linear problems. Between reading implementation notes on the web and reasoning about various issues I have chosen the implementation in this chapter, which does in fact seem to work correctly. I have yet to explore the significant amount of original literature that will likely definitively explain the discrepancies. I would like to leave this here in some form even if I do find an explanation that reconciles the various differences, as if I got confused by these books than probably others will as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [1] Mackenzie, Dana. *Ensemble Kalman Filters Bring Weather Models Up to Date* Siam News,  Volume 36, Number 8, October 2003. http://www.siam.org/pdf/news/362.pdf\n",
    "\n",
    "- [2]  Brown, Robert Grover, and Patrick Y.C. Hwang. *Introduction to Random Signals and Applied Kalman Filtering, With MATLAB® excercises and solutions.* Wiley, 2012.\n",
    "\n",
    "- [3] Crassidis, John L., and John L. Junkins. *Optimal estimation of dynamic systems*. CRC press, 2011."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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